Working with big volumes of data is a complicated task, but it's even harder if you have to do everything in real time and try to figure it all out yourself. This session will use practical examples to discuss architectural best practices and lessons learned when solving real-time social media analytics, sentiment analysis, and data visualization decision-making problems with AWS. Learn how you can leverage AWS services like Amazon RDS, AWS CloudFormation, Auto Scaling, Amazon S3, Amazon Glacier, and Amazon Elastic MapReduce to perform highly performant, reliable, real-time big data analytics while saving time, effort, and money. Gain insight from two years of real-time analytics successes and failures so you don't have to go down this path on your own.

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November 13, 2014 | Las Vegas, NV
ARC202Real-World Real-Time Analytics
Gustavo Arjones| @arjones
CTO, Socialmetrix
Sebastian Montini | @sebamontini
Solutions Architect, Socialmetrix

2. •SaaS Company—since 2008
•Social media analytics track and measure activity of brands and personality, providing information to market research and brand comparison
•Multilanguagetechnology(English, Portuguese, and Spanish)
•Leader in Latin America, with operations in 5 countries, customers in Latin Americaand US
•1 out of 34 Twitter Certified Program worldwide

3. Our customers

6. Ranking
Brand 1
Brand 2
Brand 3
Q2
Q3
Q2
Q3
Q2
Q3
1°
Flavor
Breakfast
Flavor
Flavor
Advertising
Flavor
2°
Healthy
Flavor
Packaging
Brand I love
Flavor
Breakfast
3°
Components
Components
Healthy
Packaging
Healthy
Healthy
4°
Advertising
Healthy
Components
Addiction
Components
Advertising
5°
Enquires
Desire
Prices
Consumption
Prices
Components
TOTAL
1.401
8.189
463
5.519
1.081
2.445
Share of topics
Which conversations are my brand and my competitors’ brands driving?

7. smx.io/reinvent
#reinvent

8. Challenges

9. Challenges: Variety
•Different data sources
•Different API
•SLA
•Method (pull or push)
•Rate-limit, backoff strategy

10. Challenges: Velocity
•Updates every second
•Top users, top hashtags each minute
•After event analysis are made with batch over complete dataset
•Spikes of 20,000+ tweets per minute

11. Last TV Debate
Results Announced
Challenges: Velocity

12. Challenges: Meaning
•Disambiguation
•DataEnrichment
–Demographics
–Sentiment
–Influencers
•Humananalysis
PAN
Orange Telecom
Oi Telecom
Hi!

13. Challenges: Alert and report
•Clear and understandable UI
•Slice-dice for business (not BI experts)
•Real-time alerts for anomalies

14. Architecture evolution

15. Drivers for architecture evolution
•More customers, bigger customers
•Add new features
•Keep costsunder control

16. Architecture evolution
0
20
40
60
80
100
120
#1 #2 #3 #4
Active Customers

17. Architecture—1stiteration
What we needed:
•Complete data isolation
•Trying different solutions/offerings

18. Architecture—1stiteration
What we did:
•All-in-one approach
•Multi-instance architecture
•Simple vertical scalability
•MySQL performance tuning

19. Architecture—1stiteration
What we've learned:
•Multi-instance is harder to administrate, but minimizes instability impact on customers
•Vertical scalability: poor resource management
•MySQL schema changes translate into downtime

20. Architecture—2nditeration
What we needed:
•Separation of responsibilities (crawling, processing)
•Horizontal scalability
•Fast provisioning
•Cost reduction

21. Architecture—2nditeration
What we changed:
•Migrated to AWS
•RabbitMQ (Single Node)
•Replace MySQL for Amazon RDS
•AWS CloudFormation
•Auto Scaling groups

22. Architecture—2nditeration
What we've learned:
•PIOPS 
•Tuning theAuto Scaling policiescan be hard
•AWS CloudFormation: great for migration, not enough for daily ops

23. Architecture—3rditeration
What we needed:
•Delivernew features (NRT, more complex analytics)
•Scalefast
•Be resilient against failure
•Addingand improvingdata sources
•Keepcosts under control (always)

24. Architecture—3rditeration
What we changed:
•Apache Storm
•RabbitMQ HA
•Amazon ElasticMapReduce (Hadoop/Hive)
•AWS CloudFormation + Chef
•Amazon Glacier + Amazon S3 lifecyclespolicies

25. Architecture—3rditeration
What we've learned:
•Spot Instances+ ReservedInstances
•Hive= SQL SQL scripts are hard to test
•BulkupsertsonAmazon RDS can be expensive (PIOPS)
•Amazon DynamoDB is great, but expensive (for our use-case)

26. Dashboard

27. Architecture—4thiteration
What we needed:
•Monitor millions of social media profiles
•Make data accessible (exploration, PoC)
•Improve UI response times
•Testing our data pipelines
•Reprocessing (faster)

28. Architecture—4th iteration
What we changed:
• Cassandra (DSE)
• MongoDB MMS
• Apache Spark

29. What we've learned:
•Leverage AWS ecosystem
•DatastaxAMI + Opscenterintegration
•MongoDBMMS: automation magic!
•Apache Spark unit testing + Amazon EC2 launch scripts
•Amazon EMR doesn’t have the latest stable versions
Architecture—4thiteration

31. Architecture evolution
-
20
40
60
80
100
120
140
160
0
20
40
60
80
100
120
#1
#2
#3
#4
Active Customers
Costs
Customers

32. Lessons learned

33. Lessons learned
•Automatesince Day 1 (CloudFormation + Chef)
•Monitor systems activity, understand your data patterns, e.g. LogStash(ELK)
•Always have a Source of Truth (Amazon S3 + Glacier)
•Make your Source of Truth searchable

34. Lessons Learned (II)
•Approximation is a good thing: HLL, CMS, Bloom
•Write your pipelines considering reprocessingneeds
•Avoidat all costs framework explosion
•AWS ecosystem allows rapid prototype

35. Socialmetrix NextGen2015

36. Architecture evolution
0
20
40
60
80
100
120
#1 #2 #3 #4
Active Customers

37. Architecture nextgen
•Reduce moving parts
•Apache Spark as central processing framework
–Realtime(Micro-batch)
–Batch-processing
•Kafka or Amazon Kinesis(Message Broker)
•Cassandra(Time-series storage)
•ElasticSearch(Content Indexer)

38. To infinity …
and beyond! Architecture evolution
0
20
40
60
80
100
120
#1 #2 #3 #4 NextGen
Active Customers

39. Gustavo Arjones, CTO
@arjones | gustavo@socialmetrix.com
Sebastian Montini, Solutions Architect
@sebamontini | sebastian@socialmetrix.com
Feedbackand QandA

40. http://bit.ly/awsevals